Storage capacity on computing devices has increased tremendously over a relatively short period of time, thereby enabling users and businesses to create and store a substantial amount of data. For example, hard drive space on today's consumer computers is in the order of hundreds of gigabytes. Servers and other higher-level devices can be associated with a significantly greater amount of storage space. As individuals and businesses have become more dependent upon electronic storage media to retain data, use of data analysis tools has increased dramatically. Many businesses today support data storage units that include astronomically large amounts of information. This information can include data relating to sales of an item, ordering, inventory, workflow, payroll, and any other suitable data. Such data can be analyzed (or mined) to learn additional information regarding customers, users, products, etc, wherein such analysis allows businesses and other users to better implement their products and/or ideas. With the advent of the Internet, and especially electronic commerce (“e-commerce”) over the Internet, the use of data analysis tools has increased. In e-commerce and other Internet and non-Internet applications, databases are generated and maintained that have large amounts of information. As stated above, information within the databases can be mined to learn trends and the like associated with the data.
Predictive models employed to predict future values with respect to particular variables in data have become prevalent and, in some instances, uncannily accurate. For example, predictive models can be employed to predict price of stock at a future point in time given a sufficient number of observed values with respect to disparate times and related stock prices. Similarly, robust predictive models can be employed to predict variables relating to weather over a number of days, weeks, months, etc. Predictions output by such models are then heavily relied upon when making decisions. For instance, an individual or corporation may determine whether to buy or sell a stock or set of stocks based upon a prediction output by the predictive model. Accordingly, it is often imperative that output predictions are relatively accurate.
Thus, while conventional time-series predictive models can be useful in connection with predicting values of variables in the future, often such models can create predictions that are far outside a realm of possibility (due to instability in underlying data or predictions output by the predictive model). There is, however, no suitable manner of testing such models to ascertain where (in time) these predictions begin to falter. For instance, a database at a trading house can be maintained that includes prices of several stocks at various instances in time. A predictive model can be run with respect to at least one stock on the data, and prices for such stock can be predicted days, weeks, months, or even years into the future using such model. However, at some point in time the predictive model can output predictions that will not be commensurate with real-world outcomes. Only after passage of time, however, will one be able to evaluate where in time the model failed (e.g., how far in the future the predictive model can output predictions with value). Therefore, a predictive model may be accurate up to a certain point in time, but thereafter may become highly inaccurate. There is no mechanism, however, for locating such position in time (e.g., three weeks out) that the predictive model begins to output faulty predictions.